This vignette gives a short introduction to CytoPipelineGUI, which is the companion package of CytoPipeline for interactive visualization of flow cytometry data pre-processing pipeline results. This vignette is distributed under a CC BY-SA license.
CytoPipelineGUI 1.0.0
To install this package, start R and enter (uncommented):
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("CytoPipelineGUI")
CytoPipelineGUI
is the companion package of CytoPipeline
, and is used for
interactive visualization of flow cytometry data pre-processing pipeline
results. It implements two shiny applications :
a shiny app for interactive comparison of flow frames that are the results
of CytoProcessingSteps of the same or different CytoPipeline experiments.
It is launched using the following statement: CytoPipelineCheckApp()
(see below);
a shiny app for interactive visualization and manual adjustments of scale
transformation objects. It is launched using the following statement:
ScaleTransformApp()
(see below).
In order to be able to show CytoPipelineGUI
in action, as a pre-requisite
we need to have created a CytoPipeline
object,
defined the different pipeline steps, and run the pipeline until completion,
so that all intermediate results can be found on a cache.
These preliminary steps are performed by the preparation code below.
# raw data
rawDataDir <- system.file("extdata", package = "CytoPipeline")
sampleFiles <- file.path(rawDataDir, list.files(rawDataDir,
pattern = "Donor"))
# output files
workDir <- suppressMessages(base::tempdir())
# pipeline configuration files (in json)
jsonDir <- rawDataDir
# creation of CytoPipeline objects
pipL_PeacoQC <-
CytoPipeline(file.path(jsonDir, "OMIP021_PeacoQC_pipeline.json"),
experimentName = "OMIP021_PeacoQC",
sampleFiles = sampleFiles)
pipL_flowAI <-
CytoPipeline(file.path(jsonDir, "OMIP021_flowAI_pipeline.json"),
experimentName = "OMIP021_flowAI",
sampleFiles = sampleFiles)
# execute PeacoQC pipeline
suppressWarnings(execute(pipL_PeacoQC, rmCache = TRUE, path = workDir))
# execute flowAI pipeline
suppressWarnings(execute(pipL_flowAI, rmCache = TRUE, path = workDir))
## Quality control for the file: Donor1
## 3.18% of anomalous cells detected in the flow rate check.
## 0% of anomalous cells detected in signal acquisition check.
## 0.12% of anomalous cells detected in the dynamic range check.
## Quality control for the file: Donor2
## 4.52% of anomalous cells detected in the flow rate check.
## 0% of anomalous cells detected in signal acquisition check.
## 0.1% of anomalous cells detected in the dynamic range check.
If you are unfamiliar with CytoPipeline
package, and you would like to
know more about these steps, it is advised that you read
the CytoPipeline
vignette, and/or that you watch the videos illustrating
the CytoPipeline
suite, which are accessible
through links included in the Demo.Rmd
vignette.
The visualization tools shown here are demonstrated on the results
of two different previously run CytoPipeline
objects.
These flow cytometry pre-processing pipeline are described in details
in the CytoPipeline
vignette. Here below is a short summary
of the illustrating dataset, as well as the pipeline steps.
The example dataset that will be used throughout this vignette is derived from a reference public dataset accompanying the OMIP-021 (Optimized Multicolor Immunofluorescence Panel 021) article (Gherardin et al. 2014).
A sub-sample of this public dataset is built-in in the CytoPipeline
package, as the OMIP021 dataset.
See the MakeOMIP021Samples.R
script for more details
on how the OMIP021
dataset was created. This script is to be found
in the script
subdirectory in the CytoPipeline
package installation path.
In our example pipeline, we assumed that we wanted to pre-process
the two samples of the OMIP021
dataset, and that we wanted to compare
what we would obtain when pre-processing these files
using two different QC methods.
In the first pre-processing pipeline, we used the flowAI
QC method
(Monaco et al. 2016), while in the second pipeline, we used the PeacoQC
method
(Emmaneel et al. 2021).
In both pipelines, the first part consisted in estimating appropriate scale
transformation functions for all channels present in the sample flowFrame
.
For this, we ran the following steps (Fig. 1):
.fcs
filesAfter this first part, pre-processing for each file, one by one, was performed.
However, depending on the choice of QC method, the order of steps
needed to be slightly different (see Fig. 2) :
Using the CytoPipelineGUI
package, it is possible to interactively inspect
intermediate results produced during the pipeline execution.
This is done through the CytoPipelineCheckApp
, which can provide
a view of the data structure, i.e. the flowFrame
,
at any step of any pipeline, as well as a comparison between any the pair of
flowFrame
state.
if (interactive()) {
CytoPipelineGUI::CytoPipelineCheckApp(dir = workDir)
}
It is difficult to extensively demonstrate specific user interactions
in a vignette, therefore live demo videos can be found from the Demo.Rmd
vignette.
However, it is possible to mimic the call to some of the shiny application
features, by using some specific CytoPipelineGUI
exported functions.
A first example below is a function call which retrieves the visuals of the workflow of a previously run pipeline:
# pre-processing workflow
expName <- "OMIP021_PeacoQC"
CytoPipelineGUI::plotSelectedWorkflow(
experimentName = expName,
whichQueue = "pre-processing",
sampleFile = sampleFiles[1],
path = workDir)